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Sen. Netw."],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>As sensor-based networks become more prevalent, scaling to unmanageable numbers or deployed in difficult to reach areas, real-time failure localisation is becoming essential for continued operation. Network tomography, a system and application-independent approach, has been successful in localising complex failures (i.e., observable by end-to-end global analysis) in traditional networks.<\/jats:p>\n          <jats:p>Applying network tomography to wireless sensor networks (WSNs), however, is challenging. First, WSN topology changes due to environmental interactions (e.g., interference). Additionally, the selection of devices for running network monitoring processes (monitors) is an NP-hard problem. Monitors observe end-to-end in-network properties to identify failures, with their placement impacting the number of identifiable failures. Since monitoring consumes more in-node resources, it is essential to minimise their number while maintaining network tomography\u2019s effectiveness. Unfortunately, state-of-the-art solutions solve this optimisation problem using time-consuming greedy heuristics.<\/jats:p>\n          <jats:p>In this article, we propose two solutions for efficiently applying Network Tomography in WSNs: a graph compression scheme, enabling faster monitor placement by reducing the number of edges in the network, and an adaptive monitor placement algorithm for recovering the monitor placement given topology changes. The experiments show that our solution is at least 1,000\u00d7 faster than the state-of-the-art approaches and efficiently copes with topology variations in large-scale WSNs.<\/jats:p>","DOI":"10.1145\/3466639","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T20:05:13Z","timestamp":1633464313000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Adaptive Monitor Placement for Near Real-time Node Failure Localisation in Wireless Sensor Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5067-1617","authenticated-orcid":false,"given":"Pamela","family":"Bezerra","sequence":"first","affiliation":[{"name":"University of Liverpool, Liverpool, United Kingdom"}]},{"given":"Po-Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"Imperial College London, South Kensington, London, United Kingdom"}]},{"given":"Julie A.","family":"McCann","sequence":"additional","affiliation":[{"name":"Imperial College London, South Kensington, London, United Kingdom"}]},{"given":"Weiren","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Warwick, Coventry, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2855198"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2879579"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2008.45"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.74.47"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472516"},{"key":"e_1_2_1_6_1","first-page":"320","article-title":"Cross-layer MAC protocol for unbiased average consensus under random interference","volume":"5","author":"Asensio-Marco C\u00e9sar","year":"2018","unstructured":"C\u00e9sar Asensio-Marco , Daniel Alonso-Rom\u00e1n , and Baltasar Beferull-Lozano . 2018 . 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